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|Title:||DENOISING AND SEGMENTATION OF ECHOCARDIOGRAPHIC IMAGES IN MULTIPLE VIEWS|
|Publisher:||ELECTRICAL ENGINEERING IIT ROORKEE|
|Abstract:||Echocardiography is the most commonly used first-line imaging modality in the assessment of cardiac chamber and valvular abnormalities. The aetiologies and the consequences of valvular abnormalities are diagnosed using multimodality transthoracic echocardiographic (TTE) images acquired in multiple views. The Doppler imaging modalities such as continuous wave Doppler (CWD) and color Doppler echocardiography along with conventional B-Mode (brightness mode) and M-Mode images are used hand-in-hand to study the anatomy of heart and its abnormalities. Speckle noise present in cross sectional TTE images makes it difficult to consistently perform delineation of the cardiac structure. It is necessary to suppress noise and enhance contrast without altering the fine details present in the images. But the technical research is more inclined towards despeckling, and segmentation of good quality B-Mode images acquired in a particular view from healthy adults. Hence, it is necessary to study the applications of despeckling and segmentation techniques for multimodality images acquired in multiple views. Current research work attempts to overcome the caveats of existing systems by integrated processing of B-Mode, CWD, and color Doppler echocardiography images. The work looks for the best despeckling and segmentation techniques suitable for multimodality echocardiographic images in multiple views. Based on the exhaustive technical and clinical literature review, the following research objectives have been framed: 1) To propose despeckling methods for the B-Mode TTE images of aortic valve and cardiac chambers, acquired in multiple views using different windows. 2) Comparative analysis of state-of-the-art despeckling techniques and texture features for the B-Mode and CWD images. 3) To propose delineation techniques for tracing the outer spectrum of CWD images. 4) Comparative analysis of segmentation techniques using the TTE images acquired in multiple views and windows. These research objectives are accomplished in the following manner. To address the issue of speckle noise in TTE images, six despeckling techniques are proposed in this thesis. The first proposed technique is based on multiscale techniques consisting of eight shrinkage techniques. The proposed multiscale techniques are employed for speckle noise reduction in the logarithmic domain, considering the approximated additive noise model of despeckling. Further, the M-band ridgelet are combined with neighborhood coefficient thresholding for the despeckling of TTE images. The performances of proposed logarithmic multiscale techniques are compared with adaptive and diffusion based filtering techniques. The denoised images are enhanced using Butterworth filter. The integrated effects of denoising and enhancement are successfully tested on active contour, region, watershed and edge based segmentation techniques. The second proposed despeckling technique is known as the hybrid triangulation moving average (TMAV) fuzzy filter. The performance of TMAV filter is fine tuned by combining it with adaptive Wiener filter. Four ii fuzzy filters have been analyzed in the logarithmic domain. In the proposed method the performances of all the four fuzzy filters are fine tuned by combining them individually with Wiener filter. The integrated fuzzy filter is the third proposed despeckling technique. It is the improved version of second proposed filter, obtained using the integration of geometric, Wiener and fuzzy filters. The hybrid homomorphic fuzzy is the fourth proposed technique. The logarithmic fuzzy and anisotropic diffusion filters are integrated in the fourth proposed method. The advantages of anisotropic diffusion and fuzzy filters are integrated in the fourth proposed hybrid homomorphic fuzzy. The hybrid posterior sampling based Bayesian estimation (PSBE) is the fifth proposed technique for despeckling of echocardiographic images in multiple views. The performance of logarithmic PSBE technique degrades considerably for images contaminated with high amount of noise. To address this issue, an adaptive filter is embedded into logarithmic PSBE and is known as the hybrid PSBE technique. The effects of denoising and enhancement on segmentation are studied using three basic techniques namely the edge, region and multistage watershed. The extreme total variation bilateral filter is the sixth proposed technique for denoising of multimodality echocardiographic images. The regularizer term of total variation (TV) filter is replaced with the bilateral (BL) term in the proposed ETVB filter. The true information is incorporated in the algorithm using Bayesian inference and probability density function. Applications of gradient projection based restoration methods have been analyzed for speckle noise reduction of TTE images. The applications of state-of-the-art despeckling filters have not been so far extensively analyzed for the Doppler echocardiographic images. In an effort to define the best despeckling filter for the B-Mode, CWD and color Doppler echocardiographic images in multiple views, a comparative study is taken up in this thesis. The applications of 48 filters are analyzed for the multimodality echocardiographic images where the performance analysis is in terms of sixteen image quality metrics, visual quality assessment and clinical validation. Both, traditional and blind assessment parameters are computed for assessment of noise suppression, edge and structure preservation. The despeckling filters are grouped into eight type’s namely local statistics, fuzzy, Fourier, multiscale, nonlinear iterative, total variation, nonlocal mean and hybrid filters. The despeckling performances of filters have also been compared in terms of 65 texture features computed from the denoised Doppler echocardiographic images. The set of features include five first order statistical (FOS), 26 spatial gray level dependence matrix (SGLDM), four gray level difference statistics (GLDS), four statistical feature matrix (SFM), six Laws texture energy measures (LTEM), four fractal dimension, two Fourier power spectrum and five neighborhood gray tone difference matrix features. All the texture features have been computed before and after the application of despeckling filters. iii The segmentation of original and the pre-processed B-Mode, CWD and color Doppler images is taken up as the next objective. This objective looks for the best delineation technique for multimodality echocardiographic images acquired in multiple views. Initially, synthetic images with different amount of intensity in-homogeneity have been delineated using segmentation techniques based on various variants of the edge, region, watershed, fuzzy, active contour and level set techniques. The applications of these techniques are also analyzed for the contouring of color Doppler, B-Mode and CWD images. The objective of segmenting the B-Mode images is to trace the inner boundaries of left ventricle and aortic valve. The color Doppler images are segmented to trace the outer boundary of regurgitant jet and compute its area. The outer spectrum of the CWD images is traced to estimate parameters like pressure half time. To begin with, the edge, region and multi-stage watershed segmentation techniques are analysed. The existing techniques such as wavelet based scale multiplication edge detection (SMED) approach, intuitionistic fuzzy divergence (IFD) based edge detection, soft thresholding, topological derivative based delineation, Magagnin and Kiruthika method have been employed for tracing the outer boundaries of the images. Further, the techniques based on active contour and level set have been employed for tracing the boundaries in the presence of intensity in-homogeneity. This set of techniques includes methods such as reaction diffusion, region scalable fitting (RSF), global minimization of active contour (GMAC), Laplacian fitting energy, statistical and variational multiphase level set (SVMLS) approach, active contour without edges, selective binary and Gaussian filtering regularized level set. The manually segmented B-Mode images are compared with results obtained on application of local region based active contour segmentation technique. The estimated parameters on manual segmentation are compared with those obtained on application of semi-automated segmentation. The analysis of segmentation techniques for CWD images is carried out using filtered as well original noisy images. The Gaussian and median filters used in Kiruthika method, Magagnin method and reaction diffusion (RD) based active contour method are replaced by the despeckling techniques in the proposed three delineation techniques namely modified Magagnin method, modified Kiruthika method and modified RD method. These basic filters are replaced by one of the ten despeckling filters namely DsFlsmv, DsFmedian, DsFhmedain, DsFad, DsFsrad, DsFlsminsc, DsFhomog, DsFwiener, DsFhomog and DsFgf4d filter. Further, performances of the modified RD method with various despeckling filters are tested using low contrast images with higher intensity in-homogeneity. The boundaries traced show that embedding of despeckling filter as replacement for the Gaussian filter in the RD method can be employed in the delineation of CWD images even in the presence of intensity in-homogeneity.|
|Appears in Collections:||DOCTORAL THESES (Electrical Engg)|
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